1. What is a Software Engineer at Character.AI?

As a Software Engineer at Character.AI, you are at the forefront of the consumer artificial intelligence revolution. Character.AI empowers over 20 million monthly users to connect, learn, and tell stories through interactive, personalized AI companions. In this role, you are not just maintaining legacy systems; you are building the core infrastructure, data flywheels, and safety alignments that define the next generation of human-computer interaction.

The engineering culture here is incredibly fast-paced and high-impact. Because Character.AI operates at a massive scale—handling tens of millions of characters and infinite conversational permutations—Software Engineers must tackle unique challenges in distributed systems, data pipelines, and machine learning infrastructure. Whether you are on the AI Platform team optimizing distributed training on GPUs, or on the AI Safety & Alignment team mitigating model toxicity through Reinforcement Learning from Human Feedback (RLHF), your work directly shapes the product.

You will collaborate closely with world-class ML researchers, product managers, and infrastructure engineers. Given the company's hyper-growth and recent unicorn status, the systems you build today will need to scale exponentially tomorrow. Expect to have a significant, visible impact on the product and the broader AI landscape within your very first weeks on the job.

2. Common Interview Questions

The following questions represent the types of challenges candidates frequently face during the Character.AI interview process. While you should not memorize answers, use these to understand the pattern of evaluating scalability, domain expertise, and problem-solving.

Data and System Architecture

These questions test your ability to design scalable, fault-tolerant systems and data pipelines capable of handling millions of concurrent users and massive data streams.

• Design a real-time data pipeline to capture user feedback (thumbs up/down) on AI responses and feed it into a training dataset.
• How would you architect a rate-limiting service for our public API to prevent abuse while ensuring low latency for legitimate users?
• Walk me through the design of a system that serves personalized character recommendations to 20 million monthly active users.
• How do you handle schema evolution in a massive BigQuery data warehouse without disrupting downstream ML training jobs?
• Design a distributed system to coordinate batch inference jobs across thousands of GPUs.

Machine Learning and AI Safety

If you are on the research or safety track, expect questions that probe your understanding of model behavior, alignment techniques, and evaluation metrics.

• Explain how you would implement RLHF from scratch. What are the most common pitfalls in the reward modeling phase?
• How do you quantitatively measure the "creativity" versus the "safety" of a generative language model?
• Describe a technique you would use to prevent a conversational AI from leaking personally identifiable information (PII) present in its training data.
• What strategies would you use to debug a model that suddenly starts exhibiting toxic behavior after a recent fine-tuning run?
• How do you optimize PyTorch training loops to maximize GPU utilization when dealing with highly variable sequence lengths?

Coding and Algorithms

These questions evaluate your raw programming ability, focus on optimal time/space complexity, and comfort with data manipulation.

• Write a function to find the longest common substring among a massive batch of chat logs.
• Implement a thread-safe LRU cache to store recent conversation contexts for quick retrieval.
• Given a stream of incoming chat messages, write an algorithm to maintain a sliding window of the top 10 most frequently used words.
• Write a program to deserialize a custom binary format used for storing model weights into a usable Python object.
• Implement an algorithm to efficiently merge multiple sorted streams of log data based on timestamps.

3. Getting Ready for Your Interviews

Preparation for a Software Engineering role at Character.AI requires a balance of strong foundational computer science skills and domain-specific knowledge in AI, data, or distributed systems.

Technical Execution and Coding You must demonstrate the ability to write clean, production-ready code. Interviewers will evaluate your fluency in Python, SQL, or Go, focusing on your ability to solve complex algorithmic challenges efficiently. Strong candidates write code that is not only functionally correct but also maintainable and scalable.

System Design and Architecture Character.AI operates at an immense scale. You will be evaluated on your ability to design robust, fault-tolerant systems in cloud environments, particularly GCP. You should be able to navigate tradeoffs between latency, throughput, and cost, especially when designing data pipelines or serving large language models.

Domain Expertise (Data or ML Safety) Depending on your specific track, interviewers will probe your specialized knowledge. For AI Platform roles, this means deep-diving into batch and streaming data pipelines (Spark, Beam, BigQuery). For Safety & Alignment roles, this involves demonstrating a strong grasp of RLHF, model evaluation methodologies, and adversarial testing.

Execution and "Get Things Done" Mindset Character.AI values proactive problem solvers who thrive in ambiguity. Interviewers will look for behavioral signals that show you can independently drive projects to completion, collaborate effectively across research and engineering lines, and prioritize tasks that deliver the highest user impact.

4. Interview Process Overview

The interview process at Character.AI is designed to be rigorous, fast-paced, and highly relevant to the actual work you will do. It typically begins with an initial recruiter phone screen to align on your background, interests, and the specific engineering track (e.g., Platform vs. Research/Safety) that best fits your profile.

Following the recruiter screen, you will move to a technical phone screen. This usually involves a live coding session via CoderPad or a similar platform, focusing on data structures, algorithms, or practical data manipulation tasks. The goal here is to establish a strong baseline of technical competency and problem-solving speed.

If successful, you will be invited to a virtual onsite loop. This comprehensive stage consists of four to five distinct rounds. You can expect a mix of deep-dive coding interviews, a specialized system or data architecture design round, a domain-specific technical deep dive (such as ML infrastructure or AI safety), and a behavioral round with an engineering manager. The process is highly collaborative, and interviewers want to see how you communicate your thought process when faced with open-ended, complex problems.

This timeline illustrates the typical progression from your initial application to the final offer stage. Use this visual to pace your preparation, ensuring you allocate sufficient time to practice both your hands-on coding skills for the early rounds and your high-level architectural thinking for the onsite loop.

5. Deep Dive into Evaluation Areas

To succeed in the onsite interviews, you must demonstrate depth across several core technical domains. Character.AI tailors these rounds heavily based on whether you are interviewing for a Platform/Data role or a Research/Safety role.

Data Infrastructure and Pipelines

For engineers focusing on the AI Platform, the ability to build and scale the "data flywheel" is paramount. You will be evaluated on your experience designing robust data warehousing solutions and scalable pipelines that feed directly into ML model training. Strong performance means designing systems that can handle massive throughput while ensuring data quality and alignment.

Be ready to go over:

• Batch and Stream Processing – Designing pipelines using tools like Apache Beam, Spark, and Ray.
• Data Warehousing – Structuring data efficiently in BigQuery and using dbt for transformations.
• Cloud Infrastructure – Managing containerized deployments using Docker, Kubernetes, and Terraform on GCP.
• Advanced concepts (less common) – Optimizing GPU utilization for data preprocessing, custom orchestration for distributed ML training.

Example questions or scenarios:

• "Design a scalable data pipeline that ingests millions of user chat logs, sanitizes the data, and prepares it for an RLHF training job."
• "How would you optimize a slow-running Spark job that is causing bottlenecks in our nightly model training cycle?"
• "Walk me through how you would set up a robust CI/CD pipeline for updating our data transformations in dbt."

Machine Learning Integration and Safety

If you are interviewing for a Research Engineering or Safety role, interviewers will focus on your ability to bridge theoretical ML research with production systems. Strong candidates will show a deep understanding of modern transformer architectures and the practical challenges of aligning them with human values.

Be ready to go over:

• Model Evaluation – Developing metrics to assess model toxicity, bias, and alignment.
• RLHF and Fine-Tuning – Implementing and scaling reinforcement learning from human feedback pipelines.
• Adversarial Testing – Designing systems to proactively uncover vulnerabilities or "jailbreaks" in LLMs.
• Advanced concepts (less common) – Explainable AI (XAI) techniques, distributed training strategies for models with billions of parameters.

Example questions or scenarios:

• "How would you design a system to automatically evaluate a new LLM checkpoint for harmful behavior before it is deployed to production?"
• "Explain the tradeoffs between using supervised fine-tuning versus RLHF for reducing model toxicity."
• "Describe a methodology for conducting adversarial testing on a conversational AI agent at scale."

Coding and Algorithmic Problem Solving

Regardless of your track, you must pass rigorous coding rounds. Character.AI evaluates your ability to write clean, optimized code under pressure. Strong performance involves not just getting the right answer, but communicating your assumptions, analyzing time and space complexity, and writing code that handles edge cases gracefully.

Be ready to go over:

• Data Structures – Advanced use of hash maps, trees, graphs, and heaps.
• Algorithms – Graph traversal (BFS/DFS), dynamic programming, and string manipulation (highly relevant for text/token processing).
• Practical Implementation – Writing Python or Go code that interacts with APIs, parses complex JSON structures, or implements basic data processing logic.

Example questions or scenarios:

• "Write a function to efficiently parse and aggregate a massive stream of user interaction tokens."
• "Implement an algorithm to detect cycles in a dependency graph for a data pipeline orchestrator."
• "Given a highly nested JSON object representing a character's memory state, write a script to extract and flatten specific interaction features."

6. Key Responsibilities

As a Software Engineer at Character.AI, your daily responsibilities will directly impact the core product experience. For Platform Engineers, your primary focus will be activating the "data flywheel." This involves building the crucial tooling and datasets that empower the research team to train state-of-the-art models. You will design, deploy, and maintain robust data pipelines using Spark, Beam, and BigQuery, ensuring that massive volumes of user interaction data are processed efficiently and securely.

For Research Engineers focused on Safety and Alignment, your day-to-day work centers on making AI models robust, honest, and harmless. You will write high-quality training and production-facing code to test hypotheses, implement novel evaluation methodologies, and conduct adversarial testing. You will frequently run experiments using RLHF and fine-tuning techniques to mitigate biases and toxicity.

Regardless of your specific focus, cross-functional collaboration is a massive part of the job. You will work hand-in-hand with ML researchers, product managers, and infrastructure teams. Because Character.AI is growing rapidly, you will also be responsible for managing cloud infrastructure (primarily GCP), setting up containerization with Docker and Kubernetes, and writing infrastructure-as-code using Terraform to ensure the systems you build can scale to meet the demands of tens of millions of users.

7. Role Requirements & Qualifications

Character.AI looks for engineers who possess a strong blend of software engineering rigor and an understanding of the modern AI landscape. The ideal candidate is highly proactive and thrives in an environment where they can take ownership of large, ambiguous problem spaces.

• Must-have skills – A B.A.S. in Computer Science or equivalent experience. At least 5+ years of experience in data engineering or backend software development within a consumer-facing tech company. Exceptional proficiency in Python and SQL. Deep experience building and managing infrastructure in a cloud environment, particularly GCP.
• Domain-specific Must-haves – For Platform roles: extensive experience with BigQuery, dbt, Ray, Beam, and Spark. For Safety roles: a PhD or equivalent experience in ML/CS, strong understanding of transformers, RLHF, and experience working with GPUs (training, serving, debugging).
• Nice-to-have skills – Experience setting up containerization and orchestration with Docker and Kubernetes. Familiarity with PyTorch. Experience writing and maintaining Golang and Terraform code. For safety roles, publications in relevant academic journals or experience with explainable AI (XAI) are highly valued.
• Soft skills – Excellent problem-solving abilities, strong communication skills for cross-functional collaboration, and a distinct "get things done" mindset. You must be comfortable operating independently and driving projects from conception to production.

8. Frequently Asked Questions

Q: How much prior Machine Learning experience is required for the Software Engineer role? It depends heavily on the track. For AI Platform and Data Engineering roles, deep ML expertise is not strictly required, though you must understand how ML models consume data and how to build infrastructure to support them (e.g., PyTorch familiarity). For Research Engineering and Safety roles, deep ML expertise, specifically with transformers and RLHF, is an absolute requirement.

Q: What is the company culture like at Character.AI? The culture is highly autonomous, fast-paced, and impact-driven. Because the company is experiencing hyper-growth, there is a strong emphasis on a "get things done" mindset. Engineers are expected to be proactive, identify bottlenecks, and ship solutions without waiting for top-down direction.

Q: How should I prioritize my preparation time? Focus first on ensuring your core coding and algorithm skills are flawless, as you cannot pass the technical screen without them. Next, dedicate significant time to System Design or Data Architecture, specifically focusing on cloud environments (GCP) and massive scale. Finally, review your domain-specific knowledge (Spark/Beam for Data, RLHF/Evaluation for Safety).

Q: How long does the interview process typically take? The end-to-end process usually takes between two to four weeks, depending on interviewer availability and how quickly you complete the initial technical screens. The recruiting team is generally highly responsive and moves quickly for strong candidates.

Q: Are these roles remote or in-office? These specific Software Engineering roles are based in Redwood City, CA. Character.AI places a strong emphasis on in-person collaboration, especially given the tight feedback loops required between engineering and research teams.

9. Other General Tips

• Bias toward action and impact: During behavioral interviews, emphasize moments in your career where you identified a problem and independently drove the solution. Character.AI wants builders who do not need hand-holding.
• Clarify constraints in System Design: Never start drawing boxes on a whiteboard without asking clarifying questions. At Character.AI's scale, knowing whether a system needs to handle 10,000 requests per second versus 1,000,000 requests per second entirely changes the architecture.

• Brush up on Cloud Native concepts: Even if you are applying for an ML-heavy role, demonstrating a solid understanding of Docker, Kubernetes, and Terraform will set you apart. Infrastructure is everyone's responsibility in a fast-growing startup.
• Communicate your tradeoffs: Whether you are writing an algorithm or designing a distributed system, always vocalize the tradeoffs you are making regarding time complexity, space complexity, latency, and cost.

• Show passion for the product: Download the Character.AI app, interact with the models, and come to the interview with informed opinions about the product experience. Interviewers love candidates who are genuinely excited about the consumer AI space.

10. Summary & Next Steps

Interviewing for a Software Engineer position at Character.AI is an opportunity to join one of the most exciting and rapidly growing companies in the consumer AI space. By focusing your preparation on scalable system design, flawless coding execution, and your specific domain expertise—whether that is building robust data flywheels or pioneering AI safety techniques—you will position yourself as a highly competitive candidate.

The compensation data above reflects the highly competitive nature of these roles. Keep in mind that total compensation at a unicorn startup like Character.AI often includes significant equity components, meaning your financial upside is directly tied to the impact you make and the growth of the company.

Approach your interviews with confidence and a builder's mindset. Remember that the interviewers are not just looking for technically proficient coders; they are looking for proactive problem solvers who can help shape the future of interactive entertainment and consumer AI. Continue to refine your skills, leverage additional insights and resources on Dataford, and step into your interviews ready to demonstrate how you can make an impact from day one.